Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds

• URL: http://arxiv.org/abs/2106.06482v2
• Date: Mon, 14 Jun 2021 13:28:20 GMT
• Title: Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds
• Authors: Emre Can Kaya, Ioan Tabus
• Abstract summary: This paper describes a novel point cloud compression algorithm that uses a neural network for estimating the coding probabilities for the occupancy status of voxels. The proposed algorithms yield state-of-the-art results on benchmark datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes a novel lossless point cloud compression algorithm that uses a neural network for estimating the coding probabilities for the occupancy status of voxels, depending on wide three dimensional contexts around the voxel to be encoded. The point cloud is represented as an octree, with each resolution layer being sequentially encoded and decoded using arithmetic coding, starting from the lowest resolution, until the final resolution is reached. The occupancy probability of each voxel of the splitting pattern at each node of the octree is modeled by a neural network, having at its input the already encoded occupancy status of several octree nodes (belonging to the past and current resolutions), corresponding to a 3D context surrounding the node to be encoded. The algorithm has a fast and a slow version, the fast version selecting differently several voxels of the context, which allows an increased parallelization by sending larger batches of templates to be estimated by the neural network, at both encoder and decoder. The proposed algorithms yield state-of-the-art results on benchmark datasets. The implementation will be made available at https://github.com/marmus12/nnctx

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